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The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation

Lin Ai, Emma Thronson, Katherine Conhaim, Amir Zeldes

arXiv:2610.19387v1cs.CL

TL;DR

Subjective definitions and judgments make proposition salience difficult to agree on and score reliably. PROPALIGN estimates graded salience from multiple summaries, and its comparisons show summary-based approaches can narrow the performance gap between a smaller model and a frontier model, though not on every metric.

  • Problem

    Proposition-salience definitions invite subjective judgments, making agreement and reliable ratio-scaled scores difficult; the choice of analysis unit is also unresolved.

    Source: §1 · p. 1

  • Method

    PROPALIGN aligns propositions to multiple summaries to estimate graded salience, and the paper releases PROPALIGNBANK and evaluates LLM-based approaches.

    Source: §1 · pp. 1–2

  • Results

    In zero-shot SAPSAL, Qwen trails Claude Sonnet-5 by 10 points versus a 27-point gap in DPSAL; GAPSAL brings them level in RMSE, but not F1 or MAE.

    Source: Why should we use summary based… · p. 8

  • Takeaways & Limitations

    Summary-based salience merits consideration for smaller models, although the best GAPSAL approach does not match Claude on F1 or MAE.

    Source: Why should we use summary based… · p. 8

    The study covers English sources, and the authors note that results, especially for LLM performance, may or may not generalize to other languages.

    Source: Limitations · p. 9

Abstract

from arXiv · show

Identification and ranking of salient propositions in documents is indirectly involved in many AI tasks, but few papers define proposition salience scores clearly, make diverse data available, or evaluate models directly on the task. This paper proposes PROPALIGN, a multiple-summary-based approach for propositional salience score estimation, and releases PROPALIGNBANK, an accompanying dataset for the method with 138K alignments of over 35K propositions. We compare several approaches to salient proposition extraction in English across a broad range of spoken and written genres, and demonstrate that naively asking models for judgments leads to inconsistencies. By contrast, graded, summary-based metrics are more stable. We further observe that while single models perform worse than humans, an ensemble of models incorporating linguistic features approaches manual annotation quality ceilings. In addition, analyses of the model outputs suggest insights into how models can extract salient information with and without the use of summaries.

1 Introduction

The paper introduces PROPALIGN to estimate graded proposition salience from multiple summaries, addressing subjective judgments and unreliable salience scales. It operationalizes propositions as EDUs and contributes a dataset, manual alignments, and model evaluations.

1 Introduction

  • Multiple summaries mitigate individual-summary variability and yield graded proposition-salience scores rather than binary judgments.The paper contrasts this with direct salience querying and single-summary alignment.Source: §1 · p. 1, Figure 1 · p. 1
  • EDUs, which roughly correspond to clauses or predications, serve as propositions because sentences can mix salient and non-salient content.Among sentences containing salient information, half included non-salient clauses in a cited study.Source: §1 · pp. 1–2
  • PROPALIGN is presented as a method and task for extracting salient propositions.Source: §1 · pp. 1–2
  • PROPALIGNBANK is introduced as a large-scale dataset for graded, summary-based proposition-salience scoring.Source: §1 · p. 2
  • The study includes tens of thousands of manual alignments and evaluates three open-weight and frontier LLMs, with an ensemble producing additional silver-standard data.Source: §1 · p. 2

2 Related work

Prior work addressed proposition salience through restricted candidate sets, content categories, and summary-related methods, but did not provide the same combination of direct graded evaluation and predictive modeling. Related summarization and RST studies explored sentence- and EDU-based salience and content extraction.

2.1 Proposition salience extraction

  • Earlier proposition-salience work used restricted verbal predicates or content categories, while prior multi-summary methods did not target salience scoring or computational prediction.The paper identifies a gap in training and development data and automatic score prediction for direct graded evaluation.Source: §2.1 · p. 2

2.2 Salience for extractive summarization

  • Extractive-summarization research has used similarity graphs, supervised neural models, transformers, graph methods, generative models, and diffusion models to identify salient content.Source: §2.2 · p. 2

2.3 RST-based approaches

  • RST-based summarization work has used EDU units and tree structure, including depth as a salience predictor and RST-informed neural or graph models.Reported approaches include EDU-based extraction and models incorporating RST relationships or tree information.Source: §2.3 · pp. 2–3

3 Data

PROPALIGNBANK uses GUM documents spanning 16 English genres and aligns EDUs to human and model-generated summaries. Manual development-set alignments and corpus annotations support evaluation, model development, and silver training data.

3 Data

  • GUM provides about 290K tokens and over 35K propositions across 16 spoken and written English genres, with five human summaries for dev/test documents and one human plus four model summaries for training documents.Source: §3 · p. 3
  • Manual development-set annotation adds about 20K EDU-to-summary alignments across five summaries per document, using good, approximate, component, and duplicate labels.Double annotations cover 25 document-summary pairs.Source: §3 · p. 3
  • The test split evaluates algorithms, the development split supplies few-shot examples and ensemble training, and the ensemble predicts alignments for silver training data.The training set has summaries and EDU segments but no alignments; GUM annotations are combined with LLM predictions for silver labels.Source: §3 · p. 3

4 Methods

The methods evaluate proposition salience using direct judgments and summary-alignment settings, with multiple summaries producing graded scores. They also describe model prompting, an ensemble, and the metrics used to assess predictions.

4.1 Settings

  • GAPSAL aggregates alignments to five summaries into a 0–5 proposition score, evaluated on score accuracy rather than which individual summary was matched.A proposition can receive the correct total score even when its alignments to particular summaries are incorrect.Source: §4.1 · p. 4

4.2 Models and prompts

  • Models receive summaries and align each EDU to summary text, classify matches, and mark duplicates; prompts compare zero-shot with in-genre one-shot examples.The direct, summary-free DPSAL baseline instead asks models to identify salient units; one-shot prompts add an example document and gold labels.Source: §4.1 · p. 3, §4.2 · p. 4

4.3 Ensembles

  • A tuned Random Forest or XGBoost metalearner combines LLM predictions with linguistic features to predict EDU-summary alignments for the manually unannotated training partition.Features include model judgments and subtypes, lexical, positional, and similarity metrics, and GUM RST annotations; tuning uses document-level five-fold cross-validation.Source: §4.3 · pp. 4–5

4.4 Metrics

  • DPSAL, SAPSAL, and GAPSAL respectively measure direct judgments, single-summary alignment, and multiple-summary graded salience; evaluation uses agreement and classification metrics, plus RMSE for GAPSAL.The deciding alignment metric ignores match subtypes because models did not reliably match human subtype judgments.Source: §4.4 · p. 5

5 Results

Summary-based approaches generally outperform direct salience judgments, while the tuned ensemble performs better than single models but remains below human performance. The results also show that model rankings depend on the evaluation metric and that summary-based prompting narrows the gap between smaller and frontier models.

5.1 DPSAL baselines and agreement

  • Summary-based approaches strongly outperform direct-prompting baselines; one-shot summary-based Qwen also outperforms one-shot GPT and Claude baselines and narrows the model gap.In the binary setting, one-shot Claude approaches the human ceiling, although humans outperform all LLMs on EDU-summary alignments.Source: §5.1 · p. 6, Table 3 · p. 6

5.2 SAPSAL performance

  • In pairwise EDU-summary alignment, in-genre one-shot examples significantly help GPT and Qwen but not Claude; Claude is the best single model for pairwise alignments.Other models are competitive on the more stable graded scores across five summaries.Source: §5.2 · p. 6

5.3 GAPSAL and Ensemble

  • Qwen has the lowest overall RMSE despite poor F1, because small score deviations and compensating false positives and false negatives can yield correct total scores.A one-point error between zero and some salience may matter more to downstream users than a larger error between scores of two and four.Source: §5.3 · pp. 6–7
  • The ensemble combining GPT and Claude binary predictions with linguistic features achieves the best F1 and MAE, significantly outperforming Claude, while human performance remains significantly higher.Because it uses GUM manual annotations, the ensemble is not usable for unseen data; its predictions were used to annotate and release the training partition.Source: Table 4 · p. 6, §5.3 · p. 7

6 Error analysis

Model performance varies by modality and document position, while qualitative errors include overgeneralized summary matches and differences in which proposition-level triggers models and annotators select. The examples show both related-content overreach and disagreement over indirect references.

6.1 Quantitative analysis

  • Claude shows no significant spoken–written difference, while GPT and Qwen have better written F1 and precision; GPT recall is better on spoken data.Low speech precision is the weakest point for all three models.Source: §6.1 · p. 7, Figure 2 · p. 8
  • Errors concentrate in the first 30% of EDUs; Claude and Qwen have over 15% of errors in the first 10%.The positional effect is most noticeable for Claude and Qwen.Source: §6.1 · p. 7

6.2 Qualitative analysis

  • In the macroeconomics example, model alignments generalize from debt and currency-related wording despite the summary omitting those specific examples.The authors identify closer, more general trigger units for the summary’s claim about money in circulation.Source: §6.2 · p. 7
  • For the cards summary, annotators accept two direct card mentions as triggers, while Claude selects an indirect reference to a set instead.The authors note that more directly related propositions were present in the document.Source: §6.2 · p. 8

7 Discussion

The discussion argues that multiple-summary alignment yields more stable, graded judgments than direct salience prompting, while acknowledging implementation cost. It also highlights model-performance gains and possible future uses of the released training data.

Why should we use summary based salience?

  • Multiple summaries make graded salience judgments more stable across models and provide a more explainable criterion than direct querying.The authors report higher agreement for PROPALIGN than for direct prompting.Source: Why should we use summary based… · p. 8
  • In zero-shot SAPSAL, Qwen trails Claude Sonnet-5 by 10 points, versus a 27-point gap in DPSAL; with GAPSAL, Qwen approaches Claude in gradient-score RMSE but not F1.Claude retains an advantage in MAE.Source: Why should we use summary based… · p. 8
  • The evaluated production pipeline requires multiple summaries per document and multiple alignment runs, making it expensive and complex compared with DPSAL.The authors contrast this setup with lower baseline scores and lower inter-model agreement for the naive alternative.Source: Why should we use summary based… · p. 8
  • The 138K-alignment silver-standard dataset could support research on proposition salience and training a single fine-tuned model, which the authors leave for future work.The passage presents these as potential uses, not evaluated outcomes.Source: Why should we use summary based… · p. 8

Limitations

The paper presupposes that summary-based salience is a desirable target and does not compare its methods with related tasks such as extractive summarization. Its evaluation is English-only, and the appendices provide prompt guidance, an output example, an RST illustration, and references to supplementary tables.

Limitations

  • The paper presupposes summary-based salience is a desirable target and does not compare its methods with related tasks such as extractive summarization.The authors distinguish salience from extractive summarization, which may prioritize intelligibility and domain-specific needs.Source: Limitations · p. 9
  • The study covers 16 spoken and written English genres, but whether its LLM findings generalize to other languages is uncertain.The full pipeline, including five abstractive summaries and alignment, is also relatively expensive compared with DPSAL.Source: Limitations · p. 9

A System Prompts for LLMs

  • The summary-based prompt treats a normal match as the same core proposition, while excluding merely relevant context; headlines and fragments can qualify as triggers.Voice, tense, word order, and phrasing differences do not disqualify a match when the information is clearly the same.Source: A System Prompts for LLMs · p. 11

B Expected Output for Summary-based Prompts

C RST example

  • The RST example depicts central coordinated constituents, cause satellites, and a discontinuous proposition interrupted by an elaboration satellite.Units [3] and [5] form the discontinuous proposition; unit [4] elaborates on a person being thanked.Source: C RST example · p. 12

D One-shot Demonstration Pairings; E Number of Original versus Normalized Qwen outputs

F Fine-grained evaluation

Fine-grained evaluation yields much lower scores than the broader evaluation, while the paper also examines salience-score error magnitudes and describes its model-tuning procedure.

F Fine-grained evaluation

G Salience Score

H.1 Finetuning

  • Models were tuned on the train partition with document-level five-fold cross-validation, selecting configurations by out-of-fold positive-class F1.The search used Optuna’s TPE with 60 trials per model.Source: §H.1 · p. 12

H.1.1 Random Forest

The appendix reports tuned Random Forest and XGBoost configurations and describes feature pruning and model-specific feature importance. Claude one-shot has the best single-model F1 except for the ensemble.

H.1.1 Random Forest

  • The final Random Forest uses 400 trees and 27 features, with a 0.465 threshold, after retraining on the full train set.Table 5 identifies development-set document-summary examples for one-shot prompting; Table 6 covers Qwen files by whether predicted EDU IDs required normalization.Source: §H.1.1 · pp. 12–13, Table 5 · p. 13, Table 6 · p. 13

H.1.2 XGBoost

  • The final XGBoost uses max depth 9, learning rate 0.054, row/column subsamples 0.864/0.750, min child weight 9, 300 boosting rounds, scale_pos_weight 8.51, threshold 0.546, and 34 features; it is retrained on the full train set.Table 7 reports precision, recall, and F1 for subtype-aware fine-grained evaluation; extra predicted EDUs are false positives, while salient EDUs assigned the wrong subtype count once as false negatives.Source: §H.1.2 · p. 13, Table 7 · p. 13

H.2 Features

  • Ensemble feature selection uses greedy backward elimination with Random Forest, and Figures 5 and 6 report feature importance for Random Forest and XGBoost, respectively.Source: §H.2 · p. 13
  • Claude one-shot is the best model by F1 score except for the ensemble; Qwen makes disproportionately many off-by-one errors, yielding better RMSE than expected but worse MAE.Source: Figure 4 · p. 14

I Position of Errors Across Documents

Errors were concentrated near document beginnings rather than showing a lost-in-the-middle pattern; error types reveal early underprediction and later overprediction of salient EDUs.

I Position of Errors Across Documents

  • Across models, errors concentrate in the first third of documents, with no observed lost-in-the-middle pattern; GPT errors fluctuate around 8%–12%.The concentration is especially notable for Claude and Qwen, while GPT makes slightly fewer errors at the beginning.Source: I Position of Errors Across Documents · p. 14, Figure 7 · p. 16
  • Figure 8’s error-type breakdown shows that most beginning-of-document errors are false negatives, while models underpredict salient EDUs early and overpredict them later.False negatives fluctuate more across document percentiles, with a notable spike just before document ends.Source: I Position of Errors Across Documents · p. 14, Figure 8 · p. 16
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